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Integrated skin sensitization assessment based on OECD methods (I): Deriving a point of departure for risk assessment
Author(s) -
Andreas Natsch,
George Frank Gerberick
Publication year - 2022
Publication title -
altex/alternatives to animal experimentation
Language(s) - English
Resource type - Journals
eISSN - 1868-8551
pISSN - 1868-596X
DOI - 10.14573/altex.2201141
Subject(s) - local lymph node assay , skin sensitization , guideline , computer science , robustness (evolution) , sensitization , data mining , applicability domain , risk assessment , confidence interval , medicine , statistics , econometrics , machine learning , quantitative structure–activity relationship , mathematics , chemistry , pathology , immunology , biochemistry , computer security , gene
Three guidelines covering key events in the skin sensitization adverse outcome pathway are endorsed by the Organisation for Economic Co-operation and Development (OECD). A recent guideline covers defined approaches (DA) to combine data from these tests for regulatory (sub)classification. The guideline methods provide continuous data that could charac-terize the sensitization potency on a more granular scale beyond (sub)classifications. We assembled a comprehensive database of in vitro and in vivo results from OECD guideline tests. Building on a previous approach using regression models, we provide quantitative models using input data from the kinetic direct peptide reactivity assay (kDPRA), the KeratinoSens™ (KS) assay, and the human cell line activation test (h-CLAT) to calculate a point of departure (PoD) in the form of a predicted local lymph node assay (LLNA) EC3 value for use in risk assessment. Predictive models include results from either two or all three assays. Detailed analysis versus in vivo data estimates redundancy between different tests and helps guide model selection. All models were tested on a set of case studies selected for their availability of multiple LLNA reference data in the OECD database. The predicted PoDs were within or close to the range of the variation of the historical LLNA data for most of these cases studies, and overall, the models predicted the in vivo value with a median fold-misprediction factor of around 2.5. The robustness of the models was characterized by comparing a comprehensive historical database versus the curated dataset provided by the OECD working group on DA.

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